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      "execution_count": 1,
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            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m39.9/39.9 MB\u001b[0m \u001b[31m13.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 17.0.0 which is incompatible.\n",
            "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 17.0.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m474.3/474.3 kB\u001b[0m \u001b[31m13.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m9.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m12.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[?25h"
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      ],
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        "!pip install -U pyarrow --quiet\n",
        "!pip install datasets transformers torch  --quiet"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "\n",
        "# Lists of product attributes\n",
        "product_types = ['Smartphone', 'Laptop', 'Headphones', 'Smartwatch', 'Tablet']\n",
        "brands = ['TechPro', 'SmartGear', 'ElectroMax', 'FutureTech', 'InnovateCorp']\n",
        "colors = ['Black', 'Silver', 'White', 'Blue', 'Red']\n",
        "materials = ['Aluminum', 'Plastic', 'Glass', 'Titanium', 'Carbon Fiber']\n",
        "\n",
        "# Feature templates\n",
        "smartphone_features = [\n",
        "    \"Display: {size} HD AMOLED screen\",\n",
        "    \"Camera: {mp}MP main camera with Night Mode\",\n",
        "    \"Battery: {capacity}mAh with fast charging\",\n",
        "    \"Storage: {storage}GB internal storage\",\n",
        "    \"Processor: {processor} chipset\"\n",
        "]\n",
        "\n",
        "laptop_features = [\n",
        "    \"{size} Full HD display\",\n",
        "    \"{processor} processor\",\n",
        "    \"{ram}GB RAM\",\n",
        "    \"{storage}GB SSD storage\",\n",
        "    \"Up to {battery_life} hours of battery life\"\n",
        "]\n",
        "\n",
        "headphones_features = [\n",
        "    \"Active Noise Cancellation\",\n",
        "    \"{playtime} hours of playtime\",\n",
        "    \"Bluetooth {bluetooth_version}\",\n",
        "    \"Water resistance: IPX{rating}\",\n",
        "    \"{driver_size}mm dynamic drivers\"\n",
        "]\n",
        "\n",
        "smartwatch_features = [\n",
        "    \"{size} OLED display\",\n",
        "    \"Heart rate and {sensor} sensor\",\n",
        "    \"Battery life: up to {battery_life} days\",\n",
        "    \"Water resistance: {water_rating}\",\n",
        "    \"GPS and {connectivity} connectivity\"\n",
        "]\n",
        "\n",
        "tablet_features = [\n",
        "    \"{size} Retina display\",\n",
        "    \"{processor} chip\",\n",
        "    \"{storage}GB storage\",\n",
        "    \"{camera}MP rear camera, {front_camera}MP front camera\",\n",
        "    \"Battery life: up to {battery_life} hours\"\n",
        "]\n",
        "\n",
        "def generate_description(product_type):\n",
        "    brand = random.choice(brands)\n",
        "    color = random.choice(colors)\n",
        "    material = random.choice(materials)\n",
        "\n",
        "    description = f\"Product: {brand} {product_type}\\n\"\n",
        "    description += f\"Color: {color}\\n\"\n",
        "    description += f\"Material: {material}\\n\"\n",
        "    description += \"Features:\\n\"\n",
        "\n",
        "    if product_type == 'Smartphone':\n",
        "        for feature in smartphone_features:\n",
        "            if \"Display\" in feature:\n",
        "                description += feature.format(size=random.choice(['5.8\"', '6.1\"', '6.5\"'])) + \"\\n\"\n",
        "            elif \"Camera\" in feature:\n",
        "                description += feature.format(mp=random.choice([12, 16, 32, 48, 64])) + \"\\n\"\n",
        "            elif \"Battery\" in feature:\n",
        "                description += feature.format(capacity=random.choice([3000, 3500, 4000, 4500])) + \"\\n\"\n",
        "            elif \"Storage\" in feature:\n",
        "                description += feature.format(storage=random.choice([64, 128, 256, 512])) + \"\\n\"\n",
        "            elif \"Processor\" in feature:\n",
        "                description += feature.format(processor=random.choice(['A14 Bionic', 'Snapdragon 888', 'Exynos 2100'])) + \"\\n\"\n",
        "\n",
        "    elif product_type == 'Laptop':\n",
        "        for feature in laptop_features:\n",
        "            if '\"' in feature:\n",
        "                description += feature.format(size=random.choice(['13.3\"', '14\"', '15.6\"', '17\"'])) + \"\\n\"\n",
        "            elif \"processor\" in feature:\n",
        "                description += feature.format(processor=random.choice(['Intel Core i5', 'Intel Core i7', 'AMD Ryzen 5', 'AMD Ryzen 7'])) + \"\\n\"\n",
        "            elif \"RAM\" in feature:\n",
        "                description += feature.format(ram=random.choice([8, 16, 32])) + \"\\n\"\n",
        "            elif \"storage\" in feature:\n",
        "                description += feature.format(storage=random.choice([256, 512, 1024])) + \"\\n\"\n",
        "            elif \"battery life\" in feature:\n",
        "                description += feature.format(battery_life=random.choice([8, 10, 12, 14])) + \"\\n\"\n",
        "\n",
        "    elif product_type == 'Headphones':\n",
        "        for feature in headphones_features:\n",
        "            if \"playtime\" in feature:\n",
        "                description += feature.format(playtime=random.choice([20, 24, 30, 36])) + \"\\n\"\n",
        "            elif \"Bluetooth\" in feature:\n",
        "                description += feature.format(bluetooth_version=random.choice(['5.0', '5.1', '5.2'])) + \"\\n\"\n",
        "            elif \"IPX\" in feature:\n",
        "                description += feature.format(rating=random.choice([4, 5, 7])) + \"\\n\"\n",
        "            elif \"drivers\" in feature:\n",
        "                description += feature.format(driver_size=random.choice([40, 43, 45, 50])) + \"\\n\"\n",
        "            else:\n",
        "                description += feature + \"\\n\"\n",
        "\n",
        "    elif product_type == 'Smartwatch':\n",
        "        for feature in smartwatch_features:\n",
        "            if '\"' in feature:\n",
        "                description += feature.format(size=random.choice(['1.2\"', '1.3\"', '1.4\"'])) + \"\\n\"\n",
        "            elif \"sensor\" in feature:\n",
        "                description += feature.format(sensor=random.choice(['SpO2', 'ECG', 'blood pressure'])) + \"\\n\"\n",
        "            elif \"Battery life\" in feature:\n",
        "                description += feature.format(battery_life=random.choice([2, 3, 5, 7])) + \"\\n\"\n",
        "            elif \"Water resistance\" in feature:\n",
        "                description += feature.format(water_rating=random.choice(['5 ATM', '10 ATM', 'IP68'])) + \"\\n\"\n",
        "            elif \"connectivity\" in feature:\n",
        "                description += feature.format(connectivity=random.choice(['LTE', 'Wi-Fi', 'NFC'])) + \"\\n\"\n",
        "\n",
        "    elif product_type == 'Tablet':\n",
        "        for feature in tablet_features:\n",
        "            if '\"' in feature:\n",
        "                description += feature.format(size=random.choice(['8\"', '10.2\"', '11\"', '12.9\"'])) + \"\\n\"\n",
        "            elif \"chip\" in feature:\n",
        "                description += feature.format(processor=random.choice(['A12 Bionic', 'A14 Bionic', 'M1'])) + \"\\n\"\n",
        "            elif \"storage\" in feature:\n",
        "                description += feature.format(storage=random.choice([32, 64, 128, 256])) + \"\\n\"\n",
        "            elif \"camera\" in feature:\n",
        "                description += feature.format(camera=random.choice([8, 12, 16]), front_camera=random.choice([5, 7, 12])) + \"\\n\"\n",
        "            elif \"Battery life\" in feature:\n",
        "                description += feature.format(battery_life=random.choice([10, 12, 14])) + \"\\n\"\n",
        "\n",
        "    return description\n",
        "\n",
        "# Generate dataset\n",
        "num_descriptions = 1000\n",
        "product_descriptions = [generate_description(random.choice(product_types)) for _ in range(num_descriptions)]\n",
        "\n",
        "print(f\"Generated {num_descriptions} product descriptions\")\n",
        "print(\"\\nSample product description:\")\n",
        "print(product_descriptions[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "plle_k1fbNNa",
        "outputId": "c898d8ca-c30d-4ed8-cb76-cfc39a518ca3"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Generated 1000 product descriptions\n",
            "\n",
            "Sample product description:\n",
            "Product: SmartGear Laptop\n",
            "Color: White\n",
            "Material: Glass\n",
            "Features:\n",
            "AMD Ryzen 7 processor\n",
            "16GB RAM\n",
            "1024GB SSD storage\n",
            "Up to 14 hours of battery life\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import GPT2LMHeadModel, GPT2Tokenizer, DataCollatorForLanguageModeling\n",
        "from transformers import Trainer, TrainingArguments\n",
        "from torch.utils.data import Dataset\n",
        "\n",
        "# Check if CUDA is available\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "print(f\"Using device: {device}\")\n",
        "\n",
        "# Load pre-trained model and tokenizer\n",
        "model_name = \"gpt2\"\n",
        "model = GPT2LMHeadModel.from_pretrained(model_name).to(device)\n",
        "tokenizer = GPT2Tokenizer.from_pretrained(model_name)\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "\n",
        "# Custom dataset class\n",
        "class ProductDescriptionDataset(Dataset):\n",
        "    def __init__(self, texts, tokenizer, max_length):\n",
        "        self.encodings = tokenizer(texts, truncation=True, padding=True, max_length=max_length, return_tensors=\"pt\")\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        item = {key: val[idx].clone().detach() for key, val in self.encodings.items()}\n",
        "        item['labels'] = item['input_ids'].clone()\n",
        "        return item\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.encodings.input_ids)\n",
        "\n",
        "# Prepare your dataset\n",
        "train_dataset = ProductDescriptionDataset(product_descriptions, tokenizer, max_length=128)\n",
        "\n",
        "# Data collator\n",
        "data_collator = DataCollatorForLanguageModeling(\n",
        "    tokenizer=tokenizer, mlm=False,\n",
        ")\n",
        "\n",
        "# Training arguments\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=\"./fine_tuned_model\",\n",
        "    overwrite_output_dir=True,\n",
        "    num_train_epochs=3,\n",
        "    per_device_train_batch_size=4,\n",
        "    save_steps=10_000,\n",
        "    save_total_limit=2,\n",
        "    fp16=True,  # Enable mixed precision training\n",
        "    no_cuda=False,  # Ensure GPU is used\n",
        ")\n",
        "\n",
        "# Initialize Trainer\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    data_collator=data_collator,\n",
        "    train_dataset=train_dataset,\n",
        ")\n",
        "\n",
        "# Fine-tune the model\n",
        "trainer.train()\n",
        "\n",
        "# Save the fine-tuned model\n",
        "model.save_pretrained(\"./fine_tuned_model\")\n",
        "tokenizer.save_pretrained(\"./fine_tuned_model\")\n",
        "\n",
        "# Generate a product description\n",
        "def generate_description(prompt, model, tokenizer, max_new_tokens=100):\n",
        "    # Encode the prompt\n",
        "    inputs = tokenizer.encode_plus(\n",
        "        prompt,\n",
        "        add_special_tokens=True,\n",
        "        return_tensors=\"pt\",\n",
        "        padding=True,\n",
        "        truncation=True\n",
        "    )\n",
        "    input_ids = inputs[\"input_ids\"].to(device)\n",
        "    attention_mask = inputs[\"attention_mask\"].to(device)\n",
        "\n",
        "    # Generate output\n",
        "    output = model.generate(\n",
        "        input_ids=input_ids,\n",
        "        attention_mask=attention_mask,\n",
        "        max_new_tokens=max_new_tokens,\n",
        "        num_return_sequences=1,\n",
        "        no_repeat_ngram_size=2,\n",
        "        top_k=50,\n",
        "        top_p=0.95,\n",
        "        temperature=0.7,\n",
        "        do_sample=True,\n",
        "        pad_token_id=tokenizer.eos_token_id\n",
        "    )\n",
        "\n",
        "    # Decode and return the generated text\n",
        "    return tokenizer.decode(output[0], skip_special_tokens=True)\n",
        "\n",
        "# Example usage\n",
        "fine_tuned_model = GPT2LMHeadModel.from_pretrained(\"./fine_tuned_model\").to(device)\n",
        "fine_tuned_tokenizer = GPT2Tokenizer.from_pretrained(\"./fine_tuned_model\")\n",
        "\n",
        "prompt = \"Product: TechPro Smartwatch\\nFeatures:\"\n",
        "generated_description = generate_description(prompt, fine_tuned_model, fine_tuned_tokenizer)\n",
        "print(\"Generated Product Description:\")\n",
        "print(generated_description)\n",
        "\n",
        "# Clean up\n",
        "torch.cuda.empty_cache()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 669,
          "referenced_widgets": [
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      "execution_count": 3,
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using device: cuda\n"
          ]
        },
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/accelerate/accelerator.py:494: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.\n",
            "  self.scaler = torch.cuda.amp.GradScaler(**kwargs)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='750' max='750' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [750/750 01:19, Epoch 3/3]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>0.414000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Generated Product Description:\n",
            "Product: TechPro Smartwatch\n",
            "Features:\n",
            "Heart rate and SpO2 sensor\n",
            "Battery life: up to 2 days\n",
            "Water resistance: 10 ATM\n",
            "GPS and LTE connectivity\n",
            "43mm dynamic drivers\n",
            "12MP rear camera, 5MP front camera\n",
            "Storage: 256GB internal storage\n",
            "Processor: A14 Bionic chipset\n",
            "32GB RAM\n",
            "256GB SSD storage\n"
          ]
        }
      ]
    }
  ]
}